2013年1月华北地区重霾污染过程SO_2和NO_x的CMAQ源同化模拟研究
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摘要
以2006年中国地区的INTEX-B排放清单为基础,采用CMAQ模式污染源同化方法,反演更新了2013年1月重霾污染过程华北地区的SO2和NOx排放源;应用WRF-CMAQ模式以及2006年INTEX-B初始排放源和2013年1月改进的排放源,分别模拟了1月9—15日和28—31日两次持续重霾污染过程的SO2和NO2浓度,并与华北地区47个环境监测站点实测值进行对比,重点分析了基于初始源和同化反演源的模拟效果及其改进原因;本文亦采用2012年清华大学编制的东亚地区MEIC排放清单评估了SO2和NOx同化反演源的合理性.分析结果表明:1CMAQ模式污染源同化方法可适用于重霾污染过程,即采用同化反演源模拟的SO2、NO2浓度时空变化特征与实测值较一致,而且可反映SO2、NOx排放源强的动态变化特征;2基于同化反演源的SO2、NO2浓度模拟效果明显优于2006年INTEX-B排放源,其时间变化趋势与实测值较一致,而且可模拟重霾污染过程SO2、NO2浓度的峰值;3采用反演源模拟的SO2、NO2浓度空间区分布特征与实测值较一致,而且可较好反映重污染区的极值分布特征;4经污染源同化改进后SO2、NO2模拟浓度与实测值的相关系数有所提高,误差明显减小;SO2的改进效果略优于NO2,这与污染源对两种污染物浓度的影响差异有关;5初始源中SO2、NOx排放源的空间分布和强度与2012年清华大学编制的排放源强差异较大,而同化反演源的空间分布和强度均接近于上述2012年排放源,较好反映出重点地区的高污染源分布特征.本文研究结果将为改进重霾污染过程的空气质量预报、减小自下而上建立的排放源清单不确定性、评估SO2、NOx等排放源的影响效应以及不同气象条件下区域排放源的动态调控等提供新技术途径和研究思路.
In this study,SO2 and NOxemission sources in North China during a heavy haze episode in January 2013 are retrieved using an adaptive nudging scheme implemented in the Community Multiscale Air Quality( CMAQ) modeling system based on the 2006 INTEX-B emission inventory. SO2 and NO2concentrations during two sustaining heavy haze episodes on 9—15 and 28—31 January are simulated using the Weather Research Forecasting( WRF)-CMAQ model system. Emissions are essentially from INTEX-B for 2006 with improved emission estimates for January 2013. Simulated results are compared with the observations at 47 stations from the China National Environmental Monitoring Centre( CNEMC),and the difference caused by the two emission estimates and the advantage of nudging source over the initial emission inventory are analyzed. Inversed SO2 and NOxemissions are evaluated using the 2012 MEIC emission inventory developed by the Tsinghua University( MEIC v1. 2 inventory). Results showsthat 1 the adaptive nudging scheme based on CMAQ model is suitable for simulating the heavy haze pollution processes. Temporal and spatial variations of simulated SO2 and NO2concentrations are consistent with those of observations,and the dynamic variations of SO2 and NOxemission sources can also be captured. 2 Inversed source is obviously superior to the initial 2006 INTEX-B emission inventory for the simulation of SO2 and NO2concentrations. Temporal variation trends and peak values of simulated SO2 and NO2are in good agreement with the observations. 3 The regional SO2 and NO2distributions simulated by inversing emission sources are in accordance with the observations,with the peak values well reproduced. 4 Correlation coefficients between the simulated and observed concentrations of SO2 and NO2increase and biases decrease using the inversed emissions compared to the initial emissions. There are greater improvements in the simulation of SO2 than NO2due to different impacts of emission sources. 5 Differences in the spatial distribution and intensity of SO2 and NOxemission sources between the initial emission estimate and the MEIC v1. 2 inventory are larger,whereas the results with the inversed emission sources are close to those with the MEIC v1. 2 inventory with high emission source intensity of SO2 and NO2in the key areas being inversed. The results of this study will provide new technique and idea for improving air quality forecasting during heavy haze pollution episodes,reducing the uncertainties in the emission inventory developed by bottom-up approach,and assessing the impacts and dynamic control of regional SO2 and NOxemission sources under different weather conditions.
In this study,SO2 and NOxemission sources in North China during a heavy haze episode in January 2013 are retrieved using an adaptive nudging scheme implemented in the Community Multiscale Air Quality( CMAQ) modeling system based on the 2006 INTEX-B emission inventory. SO2 and NO2concentrations during two sustaining heavy haze episodes on 9—15 and 28—31 January are simulated using the Weather Research Forecasting( WRF)-CMAQ model system. Emissions are essentially from INTEX-B for 2006 with improved emission estimates for January 2013. Simulated results are compared with the observations at 47 stations from the China National Environmental Monitoring Centre( CNEMC),and the difference caused by the two emission estimates and the advantage of nudging source over the initial emission inventory are analyzed. Inversed SO2 and NOxemissions are evaluated using the 2012 MEIC emission inventory developed by the Tsinghua University( MEIC v1. 2 inventory). Results showsthat 1 the adaptive nudging scheme based on CMAQ model is suitable for simulating the heavy haze pollution processes. Temporal and spatial variations of simulated SO2 and NO2concentrations are consistent with those of observations,and the dynamic variations of SO2 and NOxemission sources can also be captured. 2 Inversed source is obviously superior to the initial 2006 INTEX-B emission inventory for the simulation of SO2 and NO2concentrations. Temporal variation trends and peak values of simulated SO2 and NO2are in good agreement with the observations. 3 The regional SO2 and NO2distributions simulated by inversing emission sources are in accordance with the observations,with the peak values well reproduced. 4 Correlation coefficients between the simulated and observed concentrations of SO2 and NO2increase and biases decrease using the inversed emissions compared to the initial emissions. There are greater improvements in the simulation of SO2 than NO2due to different impacts of emission sources. 5 Differences in the spatial distribution and intensity of SO2 and NOxemission sources between the initial emission estimate and the MEIC v1. 2 inventory are larger,whereas the results with the inversed emission sources are close to those with the MEIC v1. 2 inventory with high emission source intensity of SO2 and NO2in the key areas being inversed. The results of this study will provide new technique and idea for improving air quality forecasting during heavy haze pollution episodes,reducing the uncertainties in the emission inventory developed by bottom-up approach,and assessing the impacts and dynamic control of regional SO2 and NOxemission sources under different weather conditions.
引文
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Elbern H,Schmidt H,Talagrand O,et al.2000.4D-variational data assimilation with an adjoint air quality model for emission analysis[J].Environmental Modelling&Software,15(6/7):539-548
高怡,张美根.2014.2013年1月华北地区重雾霾过程及其成因的模拟分析[J].气候与环境研究,19(2):140-152
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刘峰,胡非,朱江.2005.用伴随方法求解多个工业污染源优化布局问题[J].中国科学D辑:地球科学,35(1):64-71
Ma J,van Aardenne J A.2004.Impact of different emission inventories on simulated tropospheric ozone over China:a regional chemical transport model evaluation[J].Atmospheric Chemistry and Physics,4(4):877-887
Martin R V,Jacob D J,Chance K,et al.2003.Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2columns[J].Journal of Geophysical Research,108(D17),doi:10.1029/2003JD003453
Miyazaki K,Eskes H J,Sudo K,et al.2012.Global NOxemission estimates derived from an assimilation of OMI tropospheric NO2columns[J].Atmospheric Chemistry and Physics,12(5):2263-2288
清华大学.2015.中国多尺度排放清单模型[OL].北京:清华大学.2015-05-27.http://www.meicmodel.org.
Streets D G,Bond T C,Carmichael G R,et al.2003.An inventory of gaseous and primary aerosol emissions in Asia in the year 2000[J].Journal of Geophysical Research,108(D21):8809-8820
唐晓,朱江,王自发,等.2013.城市尺度大气污染源反演系统的发展及初步应用[A]//第四届全国大气边界层物理和大气化学学术研讨会论文集[C].重庆:中科院大气物理研究所
唐孝炎,张远航,邵敏.2006.大气环境化学(第2版)[M].北京:高等教育出版社.447-449
The University of North Carolina.2014.SMOKE v3.6 User'S Manual[R].Chapel Hill:The Institute for the Environment.1-520
王丽涛,潘雪梅,郑佳,等.2012.河北及周边地区霾污染特征的模拟研究[J].环境科学学报,32(4):925-931
王跃思,姚利,王莉莉,等.2014.2013年元月我国中东部地区强霾污染成因分析[J].中国科学:地球科学,44(1):15-26
王自发,李杰,王哲,等.2014.2013年1月我国中东部强霾污染的数值模拟和防控对策[J].中国科学:地球科学,44(1):3-14
Xu X D,Xie L A,Cheng X H,et al.2008.Application of an adaptive nudging scheme in air quality forecasting in China[J].Journal of Applied Meteorology and Climatology,47(8):2105-2114
杨欣,陈义珍,刘厚凤,等.2014.北京2013年1月连续强霾过程的污染特征及成因分析[J].中国环境科学,34(2):282-288
杨文夷,李杰,朱莉莉,等.2013.我国空气污染物人为源排放清单对比[J].环境科学研究,26(7):703-711
杨一帆,张凯山.2013.突发型大气污染源位置识别反演问题的数值模拟[J].环境科学学报,33(9):2388-2394
Zhang Q,Streets D G,Carmichael G R,et al.2009.Asian emissions in2006 for the NASA INTEX-B mission[J].Atmospheric Chemistry and Physics,9(14):5131-5153
张小曳,孙俊英,王亚强,等.2013.我国雾-霾成因及其治理的思考[J].科学通报,58(13):1178-1187
Zhao B,Wang P,Ma J Z,et al.2012.A high-resolution emission inventory of primary pollutants for the Huabei region,China[J].Atmospheric Chemistry and Physics,12(1):481-501
中国环境监测总站.2012.全国城市空气质量实时发布平台[OL].北京:中国环境监测总站.2015-05-27.http://113.108.142.147:20035/emcpublish.
朱江,汪萍.2006.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用[J].大气科学,30(5):872-882
蔡旭辉,邵敏,苏芳.2002.甲烷排放源逆向轨迹反演模式研究[J].环境科学,23(5):19-24
陈军明,徐大海,朱蓉.2002.遗传算法在点源扩散浓度反演排放源强中的应用[J].气象,28(9):12-16
程兴宏.2008.空气质量模式“源同化”模型及排放源影响效应研究[D].北京:中国科学院研究生院.1-149
Cheng X H,Xu X D,Ding G A.2010.An emission source inversion model based on satellite data and its application in air quality forecasts[J].Science China Earth Sciences,53(5):752-762
Elbern H,Schmidt H,Talagrand O,et al.2000.4D-variational data assimilation with an adjoint air quality model for emission analysis[J].Environmental Modelling&Software,15(6/7):539-548
高怡,张美根.2014.2013年1月华北地区重雾霾过程及其成因的模拟分析[J].气候与环境研究,19(2):140-152
Huang R J,Zhang Y L,Bozzetti C,et al.2014.High secondary aerosol contribution to particulate pollution during haze events in China[J].Nature,514(7521):218-222
Jiang Z,Jones D B A,Kopacz M,et al.2011.Quantifying the impact of model errors on top-down estimates of carbon monoxide emissions using satellite observations[J].Journal of Geophysical Research,116:D15306,doi:10.1029/2010JD015282
靳军莉,颜鹏,马志强,等.2014.北京及周边地区2013年1-3月PM2.5变化特征[J].应用气象学报,25(6):690-700
Kopacz M,Jacob D J,Henze D K,et al.2009.Comparison of adjoint and analytical Bayesian inversion methods for constraining Asian sources of carbon monoxide using satellite(MOPITT)measurements of CO columns[J].Journal of Geophysical Research,114,D04305,doi:10.1029/2007JD009264
李灿,许黎,邵敏,等.2003.一种大气CO2源汇反演模式方法的建立及应用[J].中国环境科学,23(6):610-613
刘峰,胡非,朱江.2005.用伴随方法求解多个工业污染源优化布局问题[J].中国科学D辑:地球科学,35(1):64-71
Ma J,van Aardenne J A.2004.Impact of different emission inventories on simulated tropospheric ozone over China:a regional chemical transport model evaluation[J].Atmospheric Chemistry and Physics,4(4):877-887
Martin R V,Jacob D J,Chance K,et al.2003.Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2columns[J].Journal of Geophysical Research,108(D17),doi:10.1029/2003JD003453
Miyazaki K,Eskes H J,Sudo K,et al.2012.Global NOxemission estimates derived from an assimilation of OMI tropospheric NO2columns[J].Atmospheric Chemistry and Physics,12(5):2263-2288
清华大学.2015.中国多尺度排放清单模型[OL].北京:清华大学.2015-05-27.http://www.meicmodel.org.
Streets D G,Bond T C,Carmichael G R,et al.2003.An inventory of gaseous and primary aerosol emissions in Asia in the year 2000[J].Journal of Geophysical Research,108(D21):8809-8820
唐晓,朱江,王自发,等.2013.城市尺度大气污染源反演系统的发展及初步应用[A]//第四届全国大气边界层物理和大气化学学术研讨会论文集[C].重庆:中科院大气物理研究所
唐孝炎,张远航,邵敏.2006.大气环境化学(第2版)[M].北京:高等教育出版社.447-449
The University of North Carolina.2014.SMOKE v3.6 User'S Manual[R].Chapel Hill:The Institute for the Environment.1-520
王丽涛,潘雪梅,郑佳,等.2012.河北及周边地区霾污染特征的模拟研究[J].环境科学学报,32(4):925-931
王跃思,姚利,王莉莉,等.2014.2013年元月我国中东部地区强霾污染成因分析[J].中国科学:地球科学,44(1):15-26
王自发,李杰,王哲,等.2014.2013年1月我国中东部强霾污染的数值模拟和防控对策[J].中国科学:地球科学,44(1):3-14
Xu X D,Xie L A,Cheng X H,et al.2008.Application of an adaptive nudging scheme in air quality forecasting in China[J].Journal of Applied Meteorology and Climatology,47(8):2105-2114
杨欣,陈义珍,刘厚凤,等.2014.北京2013年1月连续强霾过程的污染特征及成因分析[J].中国环境科学,34(2):282-288
杨文夷,李杰,朱莉莉,等.2013.我国空气污染物人为源排放清单对比[J].环境科学研究,26(7):703-711
杨一帆,张凯山.2013.突发型大气污染源位置识别反演问题的数值模拟[J].环境科学学报,33(9):2388-2394
Zhang Q,Streets D G,Carmichael G R,et al.2009.Asian emissions in2006 for the NASA INTEX-B mission[J].Atmospheric Chemistry and Physics,9(14):5131-5153
张小曳,孙俊英,王亚强,等.2013.我国雾-霾成因及其治理的思考[J].科学通报,58(13):1178-1187
Zhao B,Wang P,Ma J Z,et al.2012.A high-resolution emission inventory of primary pollutants for the Huabei region,China[J].Atmospheric Chemistry and Physics,12(1):481-501
中国环境监测总站.2012.全国城市空气质量实时发布平台[OL].北京:中国环境监测总站.2015-05-27.http://113.108.142.147:20035/emcpublish.
朱江,汪萍.2006.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用[J].大气科学,30(5):872-882